Before you begin, note that, in the header, the output format of this
document is html_notebook. When you save this file, it
automatically creates another file with the same file name but with
.nb.html extension in the same directory. This is the file
you will submit as your homework solution along with the
.Rmd file.
Warnings:
- Donβt delete the
nb.html file.
- Donβt
knit your .Rmd file to
html. If you want to look at the output, just open the
nb.html in the browser. Alternatively, click on the
βPreviewβ button on top of the document.
If you delete nb.html file, you may have to create a new
.Rmd file and restart from there. If you knit your
.Rmd file to html, you will not be able to
retain any of the interactivity in the plots. This means the TA will
not be able to grade you!
The objective of this homework is to give you more practice on
interactive visualizations using plotly and
highcharter.
As always, recreate these visualizations exactly. Q1 uses
plotly while Q2-Q5 use highcharter.
Q1 (3 points)
Use mpg data set from ggplot2 to create a
static visualization and then use ggplotly() to create a
limited interactive plot.
Hint: You will need to supply only
frame. No ids used.
data(mpg, package = "ggplot2")
gg <- ggplot(mpg,
aes(x = cty, y = hwy, color = class)) +
geom_point(aes(frame = class)) +
labs(x = 'cty',
y = 'hwy') +
theme_minimal() +
theme(
legend.position = 'none'
)
ggplotly(gg)
NA
For the next four questions, you will use highcharter.
Q2 (3 points)
This example creates a heatmap similar to the one shown
here.
Use mpg data and hchart() function. We want
to create a heatmap of average highway mileage for different
class and cyl. This plot removes all the
observations with five cylinders or with 2seater class.
Also note that I am treating cyl as a character (string)
variable. This is essential for creating this plot.
I am using hc_theme_538(). Furthermore, the default
color in the heatmap is blue, which I changed using
hc_colorAxis() function that I used in the Week 10
heatmap.
data(mpg, package = "ggplot2")
mpg$cyl = as.character(mpg$cyl)
mpg2 = mpg %>%
select(cyl, class, hwy) %>%
filter(
!cyl %in% c('5') &
!class %in% c('2seater')
) %>%
group_by(class, cyl) %>%
summarise(
hwy = round(mean(hwy),2),
.groups = "drop")
stops = color_stops(colors = rev(c("#000004FF",
"#56106EFF",
"#BB3754FF",
"#F98C0AFF",
"#FCFFA4FF")))
hchart(
mpg2,
"heatmap",
hcaes(x = class, y = cyl, value = hwy),
colorKey = "hwy",
) %>%
hc_colorAxis(
min = 15,
max = 35,
stops = stops) %>%
hc_add_theme(hc_theme_538()) %>%
hc_legend(
min = 15,
max = 35,
enabled = TRUE) %>%
hc_plotOptions(
series = list(showInLegend = FALSE)
)
Q3 (3 points)
In the above plot, the tooltip shows confusing information. Below, I
modified the tooltip to present more information. The code is not at all
complicated and relies on the tooltip code we used in Week 10.
Next, I removed the X axis title and modified Y axis title.
Finally, I added a title to the plot. Note how I used four different
emojies related to cars. It doesnβt matter which car emojis you use as
long as they are related to automobiles.
data(mpg, package = "ggplot2")
mpg$cyl = as.character(mpg$cyl)
mpg2 = mpg %>%
select(cyl, class, hwy) %>%
filter(
!cyl %in% c('5') &
!class %in% c('2seater')
) %>%
group_by(class, cyl) %>%
summarise(
hwy = round(mean(hwy),2),
.groups = "drop")
stops = color_stops(colors = rev(c("#000004FF",
"#56106EFF",
"#BB3754FF",
"#F98C0AFF",
"#FCFFA4FF")))
fntltp <- JS("function(){
return 'For class ' + this.series.xAxis.categories[this.point.x] + ' with ' +
this.series.yAxis.categories[this.point.y] + ' cylinders' + ': <b>' +
Highcharts.numberFormat(this.point.value, 2)+'</b>' + ' mpg';
; }")
hchart(
mpg2,
"heatmap",
hcaes(x = class, y = cyl, value = hwy),
colorKey = "hwy",
name = "Highway Mileage"
) %>%
hc_colorAxis(
min = 15,
max = 35,
stops = stops) %>%
hc_add_theme(hc_theme_538()) %>%
hc_legend(
min = 15,
max = 35,
enabled = TRUE) %>%
hc_plotOptions(
series = list(showInLegend = FALSE)
) %>%
hc_title(text = "Highway Mileage Decreases across all the π π π π» as the Number of Cylinders Increases",
style = list(color = "black", weight = "bold")) %>%
hc_yAxis(title = list(text = "Number of Cylinders")) %>%
hc_xAxis(title = list(text = "")) %>%
hc_tooltip(formatter = fntltp)
NA
Q4 (3 points)
For this example, use a randomly selected subset of
diamonds data set from ggplot2:
?diamonds
set.seed(2020)
d1 = diamonds[sample(nrow(diamonds), 1000),]
Next use d1 to create the following plot.
I have used hc_theme_flat() for this plot.
Please use this theme for your plot too! You can add a
theme to the plot using hc_add_theme() function. Wherever
the word diamond appeared in the plot, I replaced it with the diamond
emoji.
Point colors in this graph are mapped to clarity. Check
out all the variables in this data set by typing ?diamonds
in the console.
hchart(
d1,
"scatter",
hcaes(x=carat, y=price, group=clarity)
) %>%
hc_add_theme(hc_theme_flat()) %>%
hc_xAxis(
title = list(text = "Weight of π in Carats")) %>%
hc_yAxis(
title = list(text = "Price of π")) %>%
hc_title(text = "Variation in Prices for π Increases with Carats",
style = list(color = "black", weight = "bold"))
NA
NA
Q5 (3 points)
Recreate the plot in Q2 using hchart(). I used
hc_theme_economist(). You can use any theme you want. You
can check out the themes here. I
used economics dataset from ggplot2. Learn
more about the variables in the dataset by typing
?economics in the console.
data(economics, package = "ggplot2")
econ2 = economics %>%
select(date, unemploy)
hchart(
econ2,
"line",
hcaes(x=date, y=unemploy)) %>%
hc_add_theme(hc_theme_economist()) %>%
hc_xAxis(
title = list(text = "Date")) %>%
hc_yAxis(
title = list(text = "Unemployment in '000"),
min = 2000,
max = 16000,
alignTicks = FALSE,
tickInterval = 2000) %>%
hc_title(text = "Unemployment Peaked after the Financial Crisis",
style = list(color = "black", weight = "bold", fontSize = 18),
align = 'center') %>%
hc_tooltip(pointFormat = "<div style=fill:#6794a7>β</div> Unemployment: <div style=font-weight:bold>{point.y}</div>")
NA
NA
Bonus plot (Not graded)
This is the same plot as above except if you hover mouse pointer over
the peak of unemployment, the tooltip will show more information. Once
again, this is a simple trick and doesnβt require any advanced
coding.
---
title: "Homework 3"
subtitle: "DA 6233"
author: "Add your name and abc123"
date: "25 October 2023"
output: 
  html_notebook:
    theme: cosmo
editor_options: 
  chunk_output_type: inline
---

```{r setup, include=FALSE}
library(tidyverse)
library(plotly)
library(highcharter)
library(dplyr)
knitr::opts_chunk$set(echo = TRUE)
```

Before you begin, note that, in the header, the output format of this document is `html_notebook`. When you save this file, it automatically creates another file with the same file name but with `.nb.html` extension in the same directory. This is the file you will submit as your homework solution along with the `.Rmd` file. 

<font color = "red"> 
**Warnings**: 

1) Don't delete the `nb.html` file. 
2) Don't `knit` your `.Rmd` file to `html`. If you want to look at the output, just open the `nb.html` in the browser. Alternatively, click on the "Preview" button on top of the document.

If you delete `nb.html` file, you may have to create a new `.Rmd` file and restart from there. If you knit your `.Rmd` file to `html`, you will not be able to retain any of the interactivity in the plots. *This means the TA will not be able to grade you!*
</font>


The objective of this homework is to give you more practice on interactive visualizations using `plotly` and `highcharter`. 

As always, recreate these visualizations exactly. Q1 uses `plotly` while Q2-Q5 use `highcharter`. 

## Q1 (3 points)

Use `mpg` data set from `ggplot2` to create a static visualization and then use `ggplotly()` to create a limited interactive plot.

**Hint**: You will need to supply only `frame`. No `ids` used.

```{r warning=FALSE, fig.width=9}
data(mpg, package = "ggplot2")
gg <- ggplot(mpg, 
             aes(x = cty, y = hwy, color = class)) +
  geom_point(aes(frame = class)) +
  labs(x = 'cty',
       y = 'hwy') +
  theme_minimal() +
  theme(
    legend.position = 'none'
  ) 
ggplotly(gg)

```

For the next four questions, you will use [`highcharter`](https://jkunst.com/highcharter/). 

## Q2 (3 points)
This example creates a heatmap similar to the one [shown here](https://jkunst.com/highcharter/articles/highcharter.html).

Use `mpg` data and `hchart()` function. We want to create a heatmap of average highway mileage for different `class` and `cyl`. This plot removes all the observations with five cylinders or with `2seater` class. Also note that I am treating `cyl` as a character (string) variable. This is essential for creating this plot.

I am using `hc_theme_538()`. Furthermore, the default color in the heatmap is blue, which I changed using `hc_colorAxis()` function that I used in the Week 10 heatmap. 

```{r fig.width=9, fig.height=6}
data(mpg, package = "ggplot2")
mpg$cyl = as.character(mpg$cyl)
mpg2 = mpg %>%
  select(cyl, class, hwy) %>%
  filter(
  !cyl %in% c('5') & 
  !class %in% c('2seater')
  ) %>%
  group_by(class, cyl) %>%
  summarise(
    hwy = round(mean(hwy),2),
    .groups = "drop") 

stops = color_stops(colors = rev(c("#000004FF", 
                                   "#56106EFF", 
                                   "#BB3754FF", 
                                   "#F98C0AFF", 
                                   "#FCFFA4FF")))  

hchart(
    mpg2,
    "heatmap",
    hcaes(x = class, y = cyl, value = hwy),
    colorKey = "hwy",
  )  %>%
  hc_colorAxis(
    min = 15,
    max = 35,
    stops = stops) %>%
  hc_add_theme(hc_theme_538()) %>%
  hc_legend(
    min = 15,
    max = 35,
    enabled = TRUE) %>%
  hc_plotOptions(
    series = list(showInLegend = FALSE)
  )
```

## Q3 (3 points)

In the above plot, the tooltip shows confusing information. Below, I modified the tooltip to present more information. The code is not at all complicated and relies on the tooltip code we used in Week 10.

Next, I removed the X axis title and modified Y axis title. 

Finally, I added a title to the plot. Note how I used four different emojies related to cars. It doesn't matter which car emojis you use as long as they are related to automobiles.


```{r fig.width=9, fig.height=6}
data(mpg, package = "ggplot2")
mpg$cyl = as.character(mpg$cyl)
mpg2 = mpg %>%
  select(cyl, class, hwy) %>%
  filter(
  !cyl %in% c('5') & 
  !class %in% c('2seater')
  ) %>%
  group_by(class, cyl) %>%
    summarise(
    hwy = round(mean(hwy),2),
    .groups = "drop") 

stops = color_stops(colors = rev(c("#000004FF", 
                                   "#56106EFF", 
                                   "#BB3754FF", 
                                   "#F98C0AFF", 
                                   "#FCFFA4FF")))  

fntltp <- JS("function(){
                  return 'For class ' + this.series.xAxis.categories[this.point.x] + ' with ' +
                         this.series.yAxis.categories[this.point.y] + ' cylinders' + ': <b>' +
                         Highcharts.numberFormat(this.point.value, 2)+'</b>' + ' mpg';
               ; }")

hchart(
    mpg2,
    "heatmap",
    hcaes(x = class, y = cyl, value = hwy),
    colorKey = "hwy",
    name = "Highway Mileage"
  )  %>%
  hc_colorAxis(
    min = 15,
    max = 35,
    stops = stops) %>%
  hc_add_theme(hc_theme_538()) %>%
  hc_legend(
    min = 15,
    max = 35,
    enabled = TRUE) %>%
  hc_plotOptions(
    series = list(showInLegend = FALSE)
  ) %>%
  hc_title(text = "Highway Mileage Decreases across all the  🚗 🚙 🏎 🛻 as the Number of Cylinders Increases",
           style = list(color = "black", weight = "bold")) %>%
  hc_yAxis(title = list(text = "Number of Cylinders")) %>%
  hc_xAxis(title = list(text = "")) %>%
  hc_tooltip(formatter = fntltp)

```


## Q4 (3 points)

For this example, use a randomly selected subset of `diamonds` data set from `ggplot2`:

```{r echo=TRUE}
?diamonds
set.seed(2020)
d1 = diamonds[sample(nrow(diamonds), 1000),]
```

Next use `d1` to create the following plot. 

I have used `hc_theme_flat()` for this plot. **Please use this theme for your plot too!**
You can add a theme to the plot using `hc_add_theme()` function. Wherever the word diamond appeared in the plot, I replaced it with the diamond emoji.

Point colors in this graph are mapped to `clarity`. Check out all the variables in this data set by typing `?diamonds` in the console.

```{r fig.width=9, fig.height=6}

hchart(
  d1,
  "scatter",
  hcaes(x=carat, y=price, group=clarity)
) %>%
  hc_add_theme(hc_theme_flat()) %>%
  hc_xAxis(
    title = list(text = "Weight of 💎 in Carats")) %>%
  hc_yAxis(
    title = list(text = "Price of 💎")) %>%
  hc_title(text = "Variation in Prices for 💎 Increases with Carats",
           style = list(color = "black", weight = "bold"))


```


## Q5 (3 points)

Recreate the plot in Q2 using `hchart()`. I used `hc_theme_economist()`. You can use any theme you want. You can check out the themes [here](https://jkunst.com/highcharter/articles/themes.html). I used `economics` dataset from `ggplot2`. Learn more about the variables in the dataset by typing `?economics` in the console.

```{r fig.width=9, fig.height=6}
data(economics, package = "ggplot2") 

econ2 = economics %>%
  select(date, unemploy)

hchart(
  econ2,
  "line",
  hcaes(x=date, y=unemploy)) %>%
  hc_add_theme(hc_theme_economist()) %>%
  hc_xAxis(
    title = list(text = "Date")) %>%
  hc_yAxis(
    title = list(text = "Unemployment in '000"),
    min = 2000,
    max = 16000,
    alignTicks = FALSE,
    tickInterval = 2000) %>%
  hc_title(text = "Unemployment Peaked after the Financial Crisis",
           style = list(color = "black", weight = "bold", fontSize = 18),
           align = 'center') %>%
  hc_tooltip(pointFormat = "<div style=fill:#6794a7>●</div> Unemployment: <b>{point.y}</b>")


```


## Bonus plot (Not graded)

This is the same plot as above except if you hover mouse pointer over the peak of unemployment, the tooltip will show more information. Once again, this is a simple trick and doesn't require any advanced coding. 


```{r fig.width=9, fig.height=6}


```


